This document discusses Liiikes, a Ruby on Rails web application that analyzes Dribbble shot data to determine which shots receive the most likes relative to the average. It does this by scraping player data through Dribbble's API, which is slow due to rate limits and the tree-like structure of drafted players. The document outlines how the app works and was open sourced on GitHub after being built, and thanks the creators Adam Darowski and Jeffrey Chupp.

1. Liiikes
Using statistics to find the best content on Dribbble.
Adam Darowski @adarowski
Jeffrey Chupp @semanticart

2. I drafted Craig Henry.
http://www.imarc.net/writable/users/bio_photo/craig_bio.jpg

3. He posted this.
http://dribbble.com/shots/1932-Monstahs

4. Dan’s rebound playoff.
http://dribbble.com/shots/10745-Rebound-me-

5. Craig’s rebound.
http://dribbble.com/shots/10780-Rebound-Asterisk

6. Then he did things like
http://dribbble.com/shots/28582-Distant-Observers

7. and
http://dribbble.com/shots/29848-No-Passage

8. and motherloving
http://dribbble.com/shots/31321-Chaos-Prints

9. I was proud.

10. I was proud.

11. I felt like a scout.

12. Likes — with one “i”

13. I like baseball.

14. I like baseball stats.

15. The best.

16. Likes Above Average (LAA)
✴ 1 shot with 885 likes. (yikes!) 885
✴ Average shot receives 17.4 likes. – 17.4
✴ This shot has a LAA score of 867.4. 867.4

23. Dribbble has an API
{
"players":[
{
"shots_count":7,
"twitter_screen_name":"IlustreBOB",
"avatar_url":"http://dribbble.s3.amazonaws.com/users/44645/avatars/original/Perfil.j
"likes_received_count":32,
"name":"Bruno O. Barros",
"created_at":"2011/07/08 15:59:17 -0400",
"location":"Rio de Janeiro",
"following_count":42,
"likes_count":70,
"website_url":"http://ilustrebob.com.br",
"username":"IlustreBOB",
"url":"http://dribbble.com/IlustreBOB",
"rebounds_count":0,
"draftees_count":0,
"id":44645,
"drafted_by_player_id":1,
"followers_count":26,
"comments_received_count":13,
"comments_count":46,
"rebounds_received_count":0

24. Liiikes is a:
✴ Ruby on Rails app
✴ With a little help from:

25. Swish
✴ Swish wraps Dribbble’s API

26. Getting the data
✴ Dribbble (as you know) works like a draft.
✴ Start with these two guys:

27. Getting the data
✴ Dribbble (as you know) works like a draft.
✴ Start with these two guys:
✴ Find the people they drafted.
✴ Find the people those people drafted.
✴ Find the people those people drafted.
✴ ... (continue for a long time) ...
✴ Finish

28. This is slow (and not so smart)
✴ Dribbble limits you to 60 calls per minute
✴ You’re walking a large tree of players
✴ It can’t find people whose draft-er has been
deleted

29. How slow is it?
✴ Really really slow.
✴ A recent refresh took 104 minutes and 25
seconds
✴ 4,715 API queries
But once you have the data, you can crunch the
numbers and you end up with something neat.

30. Open Sourced!
https://github.com/semanticart/liiikes

31. Thank you
Adam Darowski Jeffrey Chupp
http://darowski.com http://semanticart.com
@adarowski @semanticart
liiikes.com